U.S. patent application number 17/164683 was filed with the patent office on 2021-08-05 for customer sentiment monitoring and detection systems and methods.
This patent application is currently assigned to Macorva Inc.. The applicant listed for this patent is Macorva Inc.. Invention is credited to Nathan CHILDRESS.
Application Number | 20210241327 17/164683 |
Document ID | / |
Family ID | 1000005432018 |
Filed Date | 2021-08-05 |
United States Patent
Application |
20210241327 |
Kind Code |
A1 |
CHILDRESS; Nathan |
August 5, 2021 |
CUSTOMER SENTIMENT MONITORING AND DETECTION SYSTEMS AND METHODS
Abstract
The present technology discloses methods, systems, and
non-transitory computer-readable media for receiving ratings data,
the received ratings data comprising responses to a survey
associated with one or more customers of an enterprise and
presenting a fixed number of attributes; aggregating the received
ratings data; generating a report based on the aggregated ratings
data; and generating a navigable interface comprising the generated
report, the navigable interface accessible to an authorized user
and comprising tabs, each tab interactable to display a respective
portion of the generated report.
Inventors: |
CHILDRESS; Nathan;
(Bellaire, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Macorva Inc. |
Bellaire |
TX |
US |
|
|
Assignee: |
Macorva Inc.
Bellaire
TX
|
Family ID: |
1000005432018 |
Appl. No.: |
17/164683 |
Filed: |
February 1, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62969534 |
Feb 3, 2020 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/06398 20130101;
G06Q 30/0282 20130101; G06Q 30/0205 20130101; G06Q 30/0203
20130101; G06Q 10/06393 20130101; G06F 3/0481 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 10/06 20060101 G06Q010/06; G06F 3/0481 20060101
G06F003/0481 |
Claims
1. A method for determining customer sentiment ratings, the method
comprising: receiving ratings data, the received ratings data
comprising responses to a survey associated with one or more
customers of an enterprise and presenting a fixed number of
attributes; aggregating the received ratings data; generating a
report based on the aggregated ratings data; and generating a
navigable interface comprising the generated report, the navigable
interface accessible to an authorized user and comprising tabs,
each tab interactable to display a respective portion of the
generated report.
2. The method of claim 1, wherein the received ratings data is
uniquely associated with the one or more customers.
3. The method of claim 1, wherein the received ratings data
comprises one or more of an overall experience rating, one or more
overall experience attributes, a brand perception rating, one or
more brand perception attributes, a product experience rating, one
or more product experience attributes, an employee rating, one or
more employee attributes, or notes.
4. The method of claim 1, further comprising: analyzing attributes
from an attribute list whose attribute frequency rates are above an
attribute frequency threshold; and dynamically adjusting attribute
presentation rates in the survey based in part on the attribute
frequency rates for the attributes.
5. The method of claim 4, further comprising: using attributes
whose attribute frequency rates in open comments are above an
open-comment attribute frequency threshold to generate additions to
the attribute list.
6. The method of claim 4, further comprising: tracking the
attribute frequency rates for attributes from the attribute list;
and removing attributes from the attribute list whose attribute
frequency rates are below an attribute frequency removal
threshold.
7. The method of claim 4, further comprising: using artificial
intelligence or manual analysis combined with the survey, sales
data, employee data, or the received ratings data to guide
generation of the attribute list.
8. The method of claim 4, further comprising: using varied analysis
techniques for different geographic regions or different
demographic populations; and dynamically varying the attribute
presentation rates based in part on the varied analysis techniques,
the different geographic regions, or the different demographic
populations.
9. The method of claim 1, further comprising: receiving survey
parameters, the survey parameters identifying the one or more
customers; and sending, to accounts or devices associated with the
one or more customers, a request to respond to the survey.
10. The method of claim 1, wherein the respective portions of the
generated report displayed by the tabs contain at least one
interactable element.
11. The method of claim 10, wherein the at least one interactable
element displayed by at least one of the tabs allow the authorized
user to generate a new survey.
12. The method of claim 1, further comprising: generating
respective scores for one or more employees of the enterprise, each
respective score based at least in part on one or more responses to
the survey; categorizing the one or more employees into performance
categories based on the respective scores; and displaying the
respective scores or the performance categories associated with the
one or more employees.
13. The method of claim 12, further comprising: generating a
projected performance for the one or more employees based on the
respective scores or the performance categories.
14. The method of claim 1, further comprising: generating
respective scores for one or more branches of the enterprise, each
respective score based at least in part on or more responses to the
survey; categorizing the one or more branches into performance
categories based on the respective scores; and displaying the
respective scores or the performance categories associated with the
one or more branches.
15. The method of claim 14, further comprising: generating a
projected performance for the one or more branches based on the
respective scores or the performance categories.
16. The method of claim 1, wherein the survey includes a numeric
rating scale for quantifying a customer sentiment, and wherein the
middle number of the numeric rating scale is presented as visually
larger in a presentation of the survey.
17. The method of claim 3, wherein the received ratings data
pursuant to an employee is added to a record for the employee.
18. The method of claim 1, further comprising: generating a list of
recommended actions for an employee based on the received ratings
data and employee data; and ordering the list of recommended
actions.
19. A system for determining customer sentiment ratings, the system
comprising: one or more processors; and at least one non-transitory
computer-readable medium having stored therein instructions which,
when executed by the one or more processors, cause the one or more
processors to perform operations comprising: receiving ratings
data, the received ratings data comprising responses to a survey
and associated with one or more customers of an enterprise;
aggregating the received ratings data; generating a report based on
the aggregated ratings data; and generating a navigable interface
comprising the generated report, the navigable interface accessible
to an authorized user and comprising tabs, each tab interactable to
display a respective portion of the generated report.
20. A non-transitory computer-readable medium having stored therein
instructions which, when executed by a processor, cause the
processor to perform operations comprising: receiving ratings data,
the received ratings data comprising responses to a survey and
associated with one or more customers of an enterprise; aggregating
the received ratings data; generating a report based on the
aggregated ratings data; and generating a navigable interface
comprising the generated report, the navigable interface accessible
to an authorized user and comprising tabs, each tab interactable to
display a respective portion of the generated report.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 62/969,534, filed on Feb. 3, 2020, entitled
"Customer Sentiment Monitoring and Detection Systems and Methods,"
the content of which is incorporated herein by reference in its
entirety.
TECHNICAL FIELD
[0002] Embodiments of the present disclosure are generally related
to methods, systems, and non-transitory computer-readable media for
determining customer sentiment ratings.
BACKGROUND
[0003] Customer experience data presents a great opportunity and
challenge for today's operations. While understanding customer
experience can lead to improvements in all aspects of a business's
customer-facing practices, managing, aggregating, storing, and
retrieving customer experience data is difficult. Cohesive
treatment of customer experience data, from acquisition to
analysis, could produce streamlined and intuitive reports for
communicating actionable information to authorized users, such as
operations managers, etc., and increase business efficiency while
providing a more robust and customer-friendly market
experience.
[0004] It is with these observations in mind, among others, that
aspects of the present disclosure were concerned and developed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] In order to describe the manner in which the above-recited
and other advantages and features of the disclosure can be
obtained, a more particular description of the principles briefly
described above will be rendered by reference to specific
embodiments thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only exemplary embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0006] FIG. 1 illustrates a system architecture for monitoring and
detecting employee sentiment, according to embodiments of the
present technology;
[0007] FIG. 2 illustrates a flowchart for a method for generating
reports, according to embodiments of the present technology;
[0008] FIG. 3 illustrates a flowchart for a method for generating
aggregated ratings for employees, according to embodiments of the
present technology;
[0009] FIG. 4 illustrates a flowchart for a method for adjusting
employee ratings, according to embodiments of the present
technology;
[0010] FIG. 5 illustrates an example survey, according to
embodiments of the present technology;
[0011] FIG. 6 illustrates an example user interface, according to
embodiments of the present technology;
[0012] FIGS. 7A-B illustrate example reporting interfaces for
departments, according to embodiments of the present
technology;
[0013] FIG. 8 illustrates an example reporting interface for an
individual and team, according to embodiments of the present
technology;
[0014] FIG. 9 illustrates an example reporting interface for a
team, according to embodiments of the present technology;
[0015] FIG. 10 illustrates a flowchart for a method for associating
survey data with organizational chart information, according to
embodiments of the present technology;
[0016] FIG. 11 illustrates an example system architecture,
according to embodiments of the present technology; and
[0017] FIG. 12 illustrates an example computing system for
performing methods of the present disclosure, according to
embodiments of the present technology;
[0018] FIGS. 13-20 illustrate an example graphical user interface
(GUI) for an individual, team, or department according to
embodiments of the present technology;
[0019] FIGS. 21-22 illustrate an example graphical user interface
(GUI) for a customer according to embodiments of the present
technology; and
[0020] FIG. 23 illustrates a flowchart for a method for determining
customer sentiment ratings.
DETAILED DESCRIPTION
[0021] The disclosure of the present technology will proceed as
follows: first, the disclosure will describe a technology for
determining workforce sentiment ratings. Second, the disclosure
will describe a technology for determining customer sentiment
ratings. It is these methods, systems, and non-transitory
computer-readable media for determining customer sentiment ratings
that are the focus of the claims.
[0022] The disclosure continues with a description of a technology
for determining workforce sentiment ratings.
[0023] One aspect of the present disclosure relates to a cloud
computing based feedback and rating system provided over a web
interface enabling employees to anonymously rate each other. As
used in this disclosure, "employee" is understood to refer to any
member of a workforce in any capacity; "supervisor" is understood
to refer to any employee under whom other employees work and/or to
whom other employees report; and, "coworker" refers to other
employees within the same workforce as a referenced employee. Each
employee (e.g., including supervisors, managers, executives,
associates, etc.) may be given a rating which can be used to
determine trends for each employee and/or aggregated trends across
groups of employees (e.g., entire organization, department,
workgroup, team, etc.).
[0024] Results of the determination may be displayed in an
organizational chart ("org chart") depicting a structure and
population of each employee within a company. As a result, employee
sentiment across the organization can be ascertained, management is
able to make informed decisions regarding promotions, demotions,
raises, firings, and performance improvement plans, and Human
Resources (HR) departments are able to quickly measure employee
engagement across an entire organization. These decisions are
typically made at the sole discretion of each supervisor, without
collecting feedback from all relevant coworkers.
[0025] The employee sentiment, provided as actionable data via the
displayed org chart interface, may be used for downstream
processes. For example, determination of raises, applying strikes
to a record, identification of candidates needing coaching,
documentation of causes for termination, and identification of
employees meriting termination can be based on the actionable
data.
[0026] A survey may be provided (e.g., automatically) to employees
(e.g., as a unique link to a web application, etc.) and provide a
data intake for generating actionable data analytics. The survey
can be conducted on either mobile or desktop devices. The data
analytics may be as granular as a single employee or as aggregated
as an entirety of the organization (e.g., company-wide), as well as
by department, workgroup, team, etc. For example, if a company is
divided into a sales division and an engineering division, and the
engineering division is further divided into backend team and
frontend team, then the analysis may be performed for the whole
company, the sales division, the overall engineering division, the
backend team of the engineering division, and/or the frontend team
of the engineering division.
[0027] An authorized user, such as an employer or the like, can log
in to a web application and choose survey parameters. Survey
parameters may include, for example and without imputing
limitation, a survey start date, reporting frequency, survey
availability duration, individual employees to survey, employee
groups (e.g., workgroup, team, division, department, etc.),
etc.
[0028] The web application may generate an org chart based on a
provided org chart (e.g., by the company) and employee photographs.
The authorized user can then visually explore the generated org
chart to, for example, check for errors, etc. In some examples,
where the generated org chart does not include employees from a
previous survey, the authorized user may be prompted to provide
correction or explanation (e.g., documentation) such as whether the
respective employee retired, was fired, quit, etc. The correction
and/or explanation can then be used for further trend analysis.
[0029] Employees, either indicated by the survey parameters or
across the entire company by default, may receive an email allowing
each respective employee to directly log into the web application
and begin the survey. Employees may be asked overall company
satisfaction questions and can see a list of coworkers within the
same department who they may rate. In some examples, the employee
may add additional coworkers to rate. As an employee adds
additional coworkers, that same employee may be added to a list
provided to each additional coworker. In some examples, the list
can include the employee who rated the additional coworkers. In
some examples, this list may obfuscate which employees rated which
other employees by adding a random subset or an entire group or
department to a list to be rated by a coworker based on the
employee adding them.
[0030] A survey may be visible to different groups of users
depending on its state. For example, the survey may be in "Pending"
state after it has been configured and scheduled by an
administrator, but is not yet open for responses. In the Pending
state, the survey may be only visible to administrators. Once the
administrator opens the survey, either by manually triggering it to
be opened or by setting a timer for when the survey should open,
the survey enters an "Open" state. In the Open state, all users may
access and update their responses to the survey. Once a user
completes a survey, the survey may enter an "Admin Review" state,
and the responses may be sent to an administrator for review. If
the administrator completes the review process and deems the survey
valid, the survey then enters a "Closed" state and becomes
available for all users to view. If the administrator considers the
survey results invalid, the administrator may delete the survey,
and the survey enters a "Deleted" state such that only certain
administrators (e.g., "super" administrators, etc.) may view the
surveys. In some examples, a survey that has been in the Closed
state for a predetermined amount of time may be automatically
changed to be in the Deleted state.
[0031] Generally, the survey may visually indicate that, on
average, employees should rate coworkers an average score. For
example, where the survey provides a ranking of 1-5, the average
may be a three and the three may be located centrally along a
sequence and/or be highlighted by distinctive selection size, font
format, coloration, etc. Or, in other words, the survey may
visually indicate that a surveyed employee should on average rate
coworkers targeting an average of three. Additionally, the survey
can include for each rated coworker a list of selectable attributes
that are descriptive of that coworker such as, for example and
without imputing limitation, "angry", "indecisive", "friendly",
"creative", "uncooperative", "inflexible", "communicator",
"reliable", "vindictive", "apathetic", "enthusiastic"
"hard-working", "rude", "disorganized", "intelligent", and
"team-oriented".
[0032] In some examples, the coworker ratings are based on how much
an employee (responding to the survey) likes working with the
respective coworker. The rating will typically be a combination of
the friendliness of the coworker, willingness to help, and ability
to accomplish work (i.e., as perceived by the employee). However,
each employee may determine their own respective most important
factors for each coworker to generate data indicating which
employees are most effective at raising company satisfaction levels
overall.
[0033] Additionally, employees, such as supervisors or managers,
can view a full org chart during and after the survey via the web
application. As a result, employees may visualize and interactively
explore the company structure. While the survey is active, the
employee can select coworkers to rate directly from the org chart.
Further, as the survey progresses across all selected employees,
authorized users may view how many have completed the survey (e.g.,
as a ratio, percentage complete, total surveys completed, etc.). In
some examples, the generated org chart can be viewed by the
authorized user and a percent of employees under each manager who
have completed the survey can be viewed so that, for example,
managers can be prompted to remind their employees to complete the
survey.
[0034] The web application may include automated email processes
associated with the survey. For example, while a survey is active
for an employee, regular reminder emails may be sent to the
employee prompting completion of the survey. Additionally, the
employee may be sent an email soliciting a rating of additional
coworkers identified by the system as candidate coworkers the
employee may want to rate. Various video tutorials and reminders
(e.g., explaining anonymity, surveying process, results, interface,
etc.) may be integrated directly into the web application.
[0035] Additionally, the web application may allow manual
identification of employee's interactions with customers, or use
existing sales data to automatically identify these relationships.
The web application will then message the customers prompting them
to complete a survey to provide feedback on the interactions.
Results from these customer surveys may then be collected and
incorporated into the feedback and rating system corresponding to
each employee. Customer surveys may be sent immediately after a
transaction (i.e. for a retail purchase or a technical support
interaction) or on a periodic bases (i.e. monthly monitoring of a
business service provider to their clients).
[0036] Once the survey is complete, either due to all (e.g., a
quorum) surveyed employees completing the survey or as a result of
the survey duration completing, actionable data analytics can be
provided to, for example, senior leadership and HR. To protect
privacy, data may be displayed only where a respective sample size
is five or more (e.g., n>=5). For example, if an employee has
been rated by only a single coworker, data regarding that employee
may be withheld from being viewable. However, where an employee has
been rated by five or more coworkers, a respective average rating
and clustering of attributes selected for that employee may be
provided to HR. In some examples, the sample size threshold may be
different based on the type of data. For example, employee
attribute data may have a threshold of 15 or more individual
coworker ratings. Company-wide attributes and free comments may
have a threshold of 100 or more individual employee ratings (or
company size, etc.).
[0037] The actionable data analytics can include a score for each
employee based on an aggregation of ratings that employee received
through the survey. As part of the aggregation process, the ratings
can be weighted, for example, based on the employee that provided
them.
[0038] For example, every score may be initialized to a
predetermined average (e.g., provided by the authorized user,
etc.). For example, the predetermined average may be 8.0. Each
rating to be aggregated into the score can be converted into a
value of -1.0, -0.4, 0, +0.8, or +2.0 to result in a final score
between 7.0 and 10.0 for each employee. The converted ratings may
then be summed, and a weight may be applied to the summation based
on the number of response. For example, and without imputing
limitation, the table below may describe a weighting scheme based
on n number of responses received.
TABLE-US-00001 TABLE 1 Responses Score Weight n = [1, 5] 0.3x n =
[6, 10] 0.5x n = [11, 20] 0.7x n = [21, 30] 0.8x n = [31, 50] 0.9x
n > 50 1x
[0039] Further, where 50 or more coworkers all rate an employee, a
minimum score may be given to the employee (e.g., a converted value
of -1.0). However, where 50 or more coworkers all rate an employee,
a maximum score can be given to the employee (e.g., a converted
value of +2.0).
[0040] Once ratings have been determined, employees receiving a
maximum rating (e.g., a rating of 10.0), may be associated with an
increased weight (e.g., a factor of 1.times.) for rating given by
that employee to coworkers. In comparison, employees receiving a
minimum rating (e.g., a rating of 7.0) may have their outgoing
ratings reductively weighted (e.g., a factor of 0.25.times.).
Employees between maximum and minimum ratings may likewise receive
weightings along a corresponding sliding scale. To account for
increased influence of employees substantially more well-received
within the company than average (and, likewise, account for
decreased influence of employees substantially less well received
within the company than average), outgoing ratings for each
employee can be recalculated based on the weighted values.
[0041] Other scores reflective of overall workforce trends can also
be calculated. For example, a happiness score can be calculated
based on a scale ranging from a "100%" indicating approximately
100% of employees rating the company "5" on the survey to a "0%"
indicating approximately 100% of employees rating the company a
"1". Employee engagement can be calculated based on a percentage of
users who responded to the survey and/or rated the company a "4" or
above. In some examples, company comparisons can be conducted by
the web application to provide insight as to, for example and
without imputing limitation, engagement and happiness scores of the
company in comparison to other companies of comparable location,
industry, size, etc. Further, the survey may include plain text
fields for employees to provide additional comments and the like.
The plain text results may be summarized with a list of comments
and/or word cloud, which may limit the word/comment display to
groups of more than 50 employee surveys to preserve anonymity,
etc.
[0042] Survey results and actionable data analytics, such as the
score and/or individual ratings, can be provided to varying degree
to defined groups within a company. For example, each employee can
see anonymized ratings and/or rating(s) over time as well as what
attributes other employees have assigned to them. Employees may
also see ratings received from different coworker groupings such
as, for example and without imputing limitation, coworkers above
the employee (e.g., managers), coworkers below the employee (e.g.,
coworkers who report to the employee), inside coworkers (e.g.,
coworkers within the same department as the employee), and outside
coworkers (e.g., coworkers in different departments than the
employee), sometimes referred to as ABIO scores.
[0043] The ABIO scores can be used to automatically identify
employee types and the like. Generally, the employee types refer to
a grouping of employees by behavior such as personality, workstyle,
performance, and/or other factors that may be useful for appraising
an employee. For example, an employee who has an "Above" rating
averaging to 8.0 and "Below" and "Outside" ratings each
respectively averaging out to 8.7 or higher may be automatically
labeled as a "Silent Superstar" because the extent of the employee
contributions may not be fully known by those above them.
[0044] In some examples, an employee, such as a supervisor for
example, can also see the ratings of coworkers who report to that
respective employee (e.g., members of a team for which the
supervising employee is responsible, etc.). Ratings for other
coworkers (e.g., lateral supervisors or managers hierarchically
above the supervisor, etc.) may be hidden from the employee. As a
result, only a company chief executive officer (CEO) or equivalent
may be able to view the ratings of every employee within the
company.
[0045] The employee may view ratings of coworkers via the navigable
org chart or by a list interface. The employee can automatically
filter by employee type when viewing coworker ratings. For example,
a manager may filter by "Silent Superstar" to identify which
employees are promising and which supervisors may need additional
coaching. In another example, an employee may filter according to
overall high ratings or overall low ratings and the like.
Additionally, an employee (e.g., a manager, etc.) can view a
percentage indicating how many coworkers below them has completed
the survey.
[0046] Further, based on the survey results and actionable data
analytics, data can be aggregated to automatically generate reports
for particular employee groups. In some examples, a rating can be
generated for an entire department, which can be treated
substantially similarly to an individual employee (e.g., with
ratings given by department members and ratings received by
individual department members and/or the department as a whole).
Further, scaling factors (as discussed above) can be applied or
reapplied to the abstracted department and/or individual.
[0047] For example, department heads, HR, and administrators may
receive a report including aggregated ratings indicating how each
department likes working with employees of other departments,
internal employee satisfaction levels as either a raw value or
relative to other departments, perception indicator of a selected
department from other departments either raw or relative to other
departments, engagement level and completion rate of employees for
each department, which employees work well with each department
(e.g., a VP of an engineering department is rated very highly by
more than 50 people in a purchasing department, etc.), which
employees work poorly with each department (e.g., a VP of a
research and development department is rated poorly by more than 20
people in an accounting department, etc.). Aggregating individual
data into larger groups enables corporate issues to be identified
and addressed for department-wide cooperation levels.
[0048] In some examples, certain reports or report components may
only be available to, for example, the CEO and/or designated HR
representatives. For example, the certain reports or report
components may include, without imputing limitation, a graph of
average employee score, average number of responses, and/or average
happiness as a function of salary (e.g., in order to understand
efficacy of the company at paying the most liked employees higher
salaries, etc.), an average overall company ratings for all
employees, and ratings related to employees who have been fired,
laid off, or have resigned (e.g., ratings of their managers,
etc.).
[0049] These and other features, and characteristics of the present
technology, as well as the methods of operation and functions of
the related elements of structure, will become more apparent upon
consideration of the following description and the appended claims
with reference to the accompanying drawings, all of which form a
part of this specification, wherein like reference numerals
designate corresponding parts in the various figures. It is to be
expressly understood, however, that the drawings are for the
purpose of illustration and description only and are not intended
as a definition of the limits of the invention. As used in the
specification and in the claims, the singular form of `a`, `an`,
and `the` include plural referents unless the context clearly
dictates otherwise.
[0050] FIG. 1 is an example system 100 for generating actionable
data analytics from an automated survey. System 100 may include one
or more servers 102 having an electronic storage 122 such as a
database or other memory system and one or more processors 124 for
performing machine-readable instruction 106 to generate the
actionable data analytics.
[0051] Machine-readable instructions 106 can include a variety of
components for performing specific actions or processes in
performing automated surveys, managing the surveys, storing and
processing data produced by the surveys, and various other
functions as may be apparent to a person having ordinary skill in
the art. A survey management component 108 can perform, manage, and
prepare a survey for users to respond to via client computing
platforms 104. Client computing platforms may receive and/or
generate a user interface (UI) 105 for various operations such as
creating a survey, reviewing survey results, responding to a
survey, etc.
[0052] A report generation component 110 may access survey results
from survey management 108 or from electronic storage 122 in order
to generate reports which may be reviewed by users via client
computing platforms 104 or provided to external resources 120
(e.g., such as downstream APIs and the like). The external
resources 120 may use the survey results, for example and without
imputing limitation, to determine a probability that an employee
would perform well if promoted, or determine if an employee is at
high risk for disciplinary action. An org chart management
component 112 receives org charts from users and produces navigable
org charts associated with data from survey management 108, report
generation 110, or electronic storage 122. Further, org chart
management 112 can update produced org charts according to survey
management 108 operations by, for example and without imputing
limitation, proposing optimizations to the org chart to improve
team structure, or identifying new employees (e.g., new hires) or
employees that are no longer surveyed (e.g., employee
terminations/resignations). A scheduling service 114 may receive
scheduling instructions from client computing platforms 104 or
external resources 120 and may enforce received schedules such as
performing a survey at regular time intervals or at specified
times. An email service 116 can perform email operations supporting
the other components such as sending out survey notices, survey
links, generated reports, org charts, and the like.
[0053] FIG. 2 is an example method 200 for generating reports based
on and including actionable data analytics. Method 200 may be
performed by system 100 to generate reports and the like.
[0054] At operation 202, survey parameters are received from an
authorized user. Survey parameters may include designation of
survey participants such as specific employees, departments,
managers and/or those beneath designated managers, etc. Survey
parameters may also include timing or scheduling information (e.g.,
to be processed by scheduling service 114) for performing a survey
at specified times or a specified schedule. In some examples,
survey parameters can include specified survey questions or
formats.
[0055] At operation 204, a survey interface is generated based on
the received parameters. The survey interface may be multiple pages
long and structured for scaling to computer, mobile, smartphone,
and other device constraints.
[0056] At operation 206, participants (e.g., designated in the
survey parameters) are provided access to the survey and can be
prompted (e.g., regularly, semi-regularly, scheduled, etc.) to
complete the survey until the survey times out (e.g., expires
according to a timing parameter provided as a survey parameter).
Participants may receive access to the survey via an email, link,
text message, etc. provided by, for example, email service 116. For
example, a link to the survey may be emailed to each recipient and,
when clicked, the link can direct the recipient to a web
application accessible via mobile, desktop, smartphone, and various
other devices.
[0057] At operation 208, the survey data provided by each
participant is aggregated and processed into a report and provided
to specified employees (e.g., specified by the survey parameters).
The generated report may be provided via email (e.g., by email
service 116) and can include direct survey responses as well as
generated data based on the survey responses such as, for example
and without imputing limitation, happiness/satisfaction scores
across the whole company, cohesion information, interdepartmental
communications guidance, etc.
[0058] FIG. 3 is an example method 300 for processing survey
response data. In some examples, method 300 can be performed by
survey component 108 and the adjust scores can be used by report
generation 110.
[0059] At operation 302, ratings are received for an employee
(e.g., via survey) and a score can be set for the employee to a
user defined average. The user defined average may be provided by
an authorized user via survey parameters during survey creation
(e.g., as discussed above in reference to FIG. 2).
[0060] At operation 304, each received rating for the employee is
converted into a base value (e.g., -1.0, -0.4, 0, +0.8, +2.0 from a
five star system). The converted values base values can be used to
more efficiently aggregate or otherwise process the ratings. For
example, the converted values may make aggregation methodologies
involving summation easier by placing values along a 0-100 and
positive to negative scale.
[0061] At operation 306, the converted ratings are aggregated. In
some examples, aggregation can be accomplished via summation. In
some examples, aggregation can be performed according to certain
algorithms or averaging (e.g., mean, median, mode, etc.). At
operation 308, the aggregated ratings are weighted (e.g., a
multiplier is applied) based on how many ratings were received.
[0062] FIG. 4 is a method 400 for processing ratings for an
employee based on weighting considerations. For example, method 400
may be performed in order to take into account company size and/or
for varying influence among employees.
[0063] At operation 402, an aggregated rating is determined for an
employee (e.g., via method 300 discussed above). The aggregated
rating is determined based on surveyed coworkers of the employee
and response rate.
[0064] At operation 404, ratings (e.g., of other employees, or
coworkers) made by the employee are adjusted according to a sliding
scale based on the respective aggregated rating for said employee.
For example, ratings made by an employee with a universally high
rating may be weighted to count for double when performing a
respective aggregation process. In comparison, ratings made by an
employee with a universally minimal rating may be weighted to count
for quarter as normal (e.g., weighted by 0.25) when performing a
respective aggregation process. Once adjustments have been made for
every employee, at operation 406, each adjusted employee ratings
may be used to recalculate the employee ratings. As a result,
employee influence may be accounted for when performing aggregation
of the survey data.
[0065] FIG. 5 is an example survey 500. Survey 500 can be performed
by a computer, mobile device, and/or smartphone. Survey 500 enables
a responder to provide satisfaction information related to a job,
management, leadership, compensation, workspace, and the like.
Additionally, free comments can be provided. Survey participants
can also rate coworkers based on a 1-5 rating of satisfaction
working with the respective coworker as well as selection of words
from a descriptive word bank.
[0066] FIG. 6 is an example user page 600 that can provide a user
(e.g., an authorized user), who may also be an employee, access to
the systems and methods of this disclosure. User page 600 can
include a home page, org chart page, reports page, and
configuration page. The home page provides an overview of past,
current, and planned surveys and includes links to response rate,
results summary, detailed org charts, tabular formatted data, and
salary reports. Current surveys can be displayed with percentage
completed so far. Additionally, planned surveys may include links
to survey settings (e.g., to provide or update survey parameters)
as well as options to use a current org chart or update the org
chart.
[0067] FIG. 7A is an example department report interface 700 that
can provide a user (e.g., a manager, senior employee, etc.), a view
of ratings which have been aggregated and abstracted to a
particular department (e.g., marketing, etc.) as a whole.
Department report interface 700 can include an inter-department
ratings section 710 and a department information section 720.
[0068] Inter-department ratings section 710 may include a tabular
listing of ratings between other departments and the particular
department. Further, a company-wide average rating, both rating the
particular department and as rated by the particular department,
may be included at the top of the tabular listing. In some
examples, inter-department ratings sections can provide a
time-comparison view. Here, for example, inter-department ratings
section 710 includes ratings for two different years (e.g., to
appraise progress, etc.). In effect, inter-department ratings
section 710 enables a user to quickly view how other departments,
overall, interact with a particular department and so identify
which departments collaborate better or worse with each other.
[0069] Department information section 720 may include various
department information to, for example, contextualize
inter-department ratings section 710 and the like. Depart
information section 720 may include a tabular view. In some
examples, department information section 720 includes, for example
and without imputing limitation, department size, engagement,
happiness, completion (e.g., survey completion, etc.), and average
inter-department rating. Additionally, department information
section 720 may include information for multiple time periods
(e.g., years, quarters, etc.) as well as an indication of a change
in information, or delta, between the time periods.
[0070] FIG. 7B is an example department report interface 750 that
includes data visualizations for intuitive and fast review of
department-specific information generated via surveys (e.g., as
discussed above). Inter-departments ratings section 760 includes
further visual elements (e.g., in comparison to department report
interface 700) to indicate response strength and the like through,
for example, a circle icon that is sized according to a
relationship between the particular department and the department
listed for comparison. Further, department information section 770
includes a chart icon indicating that detailed information is
available for a particular department statistic (e.g., happiness,
management, company leadership, compensation and benefits,
workspace and tools, etc.). In some examples, the chart icon may be
interacted with to view an expanded graph view 780 which includes a
bar chart depicting a spread of responses related to a respective
department statistic.
[0071] FIG. 8 is an example reporting interface 800 for a user to
review their own ABIO score history as well as an ABIO composition
of a respective team. For example, reporting interface 800 includes
an ABIO snapshot 802 providing the user recent ratings information
and a resultant ABIO score. An ABIO history 804 provides comparison
snapshots of the user ABIO score over multiple time periods. Each
comparison snapshot is displayed as a bar chart of each sub-score
that makes up the ABIO score for the respective time period. As a
result, a user can see changes to the user ABIO score as well as
quickly appraise along which dimensions (e.g., above, below,
inside, outside, etc.) changes have taken place. Further, a team
composition section 806 shows the user which employee types are
present on a respective team and how many. The employee types are
based on respective ABIO scores for team members, which may be kept
unknown to the user in order to maintain anonymity of the data.
[0072] FIG. 9 is an example team ABIO report interface 900 for
reviewing ABIO information across an entire team for each member of
the team. An authorized user (e.g., a team lead, manager,
supervisor, etc.) can access team ABIO report interface 900 to
review ABIO scores for all members of the team. Team ABIO report
interface 900 can include a tabular view 902 in which each row is
associated with a particular employee (e.g., team member) and
columns provide identification 904, name 906, department 908, an
overall ABIO value 910, and individual ABIO component values
912-918.
[0073] More particularly, overall ABIO score 910 and individual
ABIO components values 912-918 are further broken down to
respective scores and sample size used to determine said scores.
Overall ABIO value 910 includes an overall ABIO score 910A and
respective overall ABIO sample size 910B, Above component value 912
includes an Above score 912A and respective Above sample size 912B,
Below component value 914 includes an Below score 914A and
respective Below sample size 914B, Inside component value 916
includes an Inside score 916A and respective Inside sample size
916B, and Outside component value 918 includes an Outside score
918A and respective Outside sample size 918B. As can be seen with
Below score 914A, where a sample size is insufficient to calculate
a rating for an employee (as discussed above), an associated value
may be labeled as "insig" or the like to identify that value as
uncalculated at the time due to sample size limitations.
[0074] FIG. 10 is an example method 1000 that may be used to load
and update org chart data to be used in the systems and methods
discussed herein. In step 1002, the org chart data provided by the
institution may be loaded. In some examples, the org chart data is
provided by the institution in a tree type data structure.
[0075] In step 1004, the org chart data input is flattened and
stored in the database. In step 1006, survey data is loaded into
the database and associated with the org chart data. For example,
the survey data may include survey questions that are separated
into different groups, where each group of questions is associated
with a different level of the org chart or a different branch of
the org chart.
[0076] Once the initial org chart is loaded, it could be updated in
the database. To update the org chart, the institution may load an
updated org chart in step 1008.
[0077] In step 1010, this updated org chart is flattened and
compared to the org chart currently stored in the database. In step
1012, the org chart stored in the database is updated to match the
updated org chart data.
[0078] In step 1014, survey data is loaded into the database and
associated with the updated org chart. The survey data may be the
same as the survey data loaded in step 1006, or it may be
different. Steps 1008 to 1014 may be repeated for multiple
updates.
[0079] FIG. 11 is an example system 1100. The example system 1100
comprises a front end 1120, a data store 1140, APIs 1150, and
additional data like org chart 1104, person user 1106, and the
survey raw data 1102.
[0080] The front end 1102 may be used to display data to users. The
displayed data may include an org chart with associated survey
results 1122, the survey 1124, a home page 1126, a table report
1128, a team report 1130, a department report 1134, and a comment
report 1136. The front end 1120 may also be used to receive data
input from the user. For example, the user may input responses to
the survey 1124 through the front end 1120.
[0081] The system 1100 also includes a data store 1140. The data
store 1140 may use a cloud storage system, a storage device, or
multiple storage devices. The data store 1140 includes a survey
store 1142 which stores survey data to be displayed on the front
end 1120, a person store 1144 that stores user information and org
chart data, and a division store 1146 that stores data related to a
division of a respective institution.
[0082] The system 1100 includes several different APIs. For
example, survey API 1152, person data endpoint 1154, division
result API 1156, division data 1158, and comments API 1160. The
APIs provide an interface for the various parts of the system 1100
to communicate with each other. For example, once a user inputs
survey 1124 results through the front end 1120, the results are
stored in survey store 1142.
[0083] Data from the survey store 1142 can be written into a
database as survey raw data 1102 through the survey API 1152. The
APIs 1150 may also be used to retrieve data to be displayed on the
front end. For example, the person data API 1154 may be used to
store user information 1106 and person survey result 1108 in the
person store 1144. The division result API 1156 may be used to
store institution result 1110 and division 1112 in the division
store 1146. The comments API 1160 may be used to display comments
from the survey raw data 1102 to the comment report 1136 of the
front end 1120.
[0084] FIG. 12 is an example computing system 1200 that may
implement various systems and methods discussed herein. The
computer system 1200 includes one or more computing components in
communication via a bus 1202. In one implementation, the computing
system 1200 includes one or more processors 1214. The processor
1214 can include one or more internal levels of cache 1216 and a
bus controller or bus interface unit to direct interaction with the
bus 1202. The processor 1214 may specifically implement the various
methods discussed herein. Main memory 1208 may include one or more
memory cards and a control circuit (not depicted), or other forms
of removable memory, and may store various software applications
including computer executable instructions, that when run on the
processor 1214, implement the methods and systems set out herein.
Other forms of memory, such as a storage device 1210 and a mass
storage device 1212, may also be included and accessible, by the
processor (or processors) 1214 via the bus 1202. The storage device
1210 and mass storage device 1212 can each contain any or all of
the methods and systems discussed herein.
[0085] The computer system 1200 can further include a
communications interface 1218 by way of which the computer system
1200 can connect to networks and receive data useful in executing
the methods and system set out herein as well as transmitting
information to other devices. The computer system 1200 can also
include an input device 1206 by which information is input. Input
device 1206 can be a scanner, keyboard, and/or other input devices
as will be apparent to a person of ordinary skill in the art. The
computer system 1200 can also include an output device 1204 by
which information can be output. Output device 1204 can be a
monitor, printer, USB, and/or other output devices or ports as will
be apparent to a person of ordinary skill in the art.
[0086] The system set forth in FIG. 12 is but one possible example
of a computer system that may employ or be configured in accordance
with aspects of the present disclosure. It will be appreciated that
other non-transitory tangible computer-readable storage media
storing computer-executable instructions for implementing the
presently disclosed technology on a computing system may be
utilized.
[0087] The disclosure now turns to a customer facing embodiment
utilizing aspects of the systems and methods discussed above. The
customer facing embodiment engages customers (e.g., after having
recently purchased an item from a store front, etc.) to elicit
feedback regarding their experiences via a graphical user interface
(GUI). The feedback may be aggregated, processed, and displayed,
for example and without imputing limitation, for operation managers
in a GUI respectively rendered for an operations manager or the
like.
[0088] Businesses rely on customer feedback, customer experiences,
brand experience, and product experience to increase sales per
customer, reduce churn, guide product portfolio decisions, guide
investments into better buildings vs more employees, etc. However,
it is difficult to get feedback from customers, which is why there
are secret shoppers, focus groups, etc. Giving out surveys where
one rates happiness from 1-10 and an accompanying open comment box
is the industry standard for gathering customer feedback, but few
people take surveys, and even fewer write out thoughtful open
comments that describe their whole experience.
[0089] The present technology changes feedback in a couple of key
ways. First, it uses a 1-5 scale. Customers do not actually use all
10 numbers on the traditional scale. It also makes 3 the largest
number size-wise, anchoring feedback to 3 and reserving higher
numbers for better experience. Traditional star platforms treat "5
stars" as "most everything went well", and "4 stars" as "there was
at least one problem".
[0090] Most importantly, the present technology adds clickable
attribute tags, so that businesses can get actionable positive and
negative feedback even if customers do not write open comments.
These attributes make it easy to compare across locations (30% of
Galleria customers clicked "Clean," but 90% of Mall of America
customers clicked "Clean"), trend over time, and induce categories
of feedback. Clicking attributes is fast and easy, unlike filling
out open comments.
[0091] Customers want to give feedback. They want their voice to be
heard, they want businesses to operate better and fix issues. But
customers do not have patience for long or frustrating surveys. If
you ask a series of 8 1-10 questions about attributes that the
customer had little opinion about, the feedback experience becomes
frustrating. However, if these are reduced to short words or
phrases that customers can tap on their phone or click with their
mouse, the feedback experience becomes faster, easier, and feels
enormously more satisfying.
[0092] Selecting the proper attributes to show customers is
critical to receiving appropriate feedback. This can be difficult,
as there may be several dozen attributes that the business is
interested in (cleanliness, ease of use, speed, crowdedness,
helpfulness, etc.), but different customers may feel more strongly
about different attributes. When sending out potentially millions
of surveys, manual selection of a few attributes may not be the
best method. This invention can randomize, record response rates,
and dynamically adjust the attributes shown as a function of
geography, customer demographics, or any combination thereof.
[0093] The present technology uses survey data received from
customers to generate a report, whose data is displayed for
consumption by an authorized user and shown in FIGS. 13-19. One
aspect of generating the report involves using attributes used by
customers, whether written in open comments or selected from a
subset of attributes displayed on a survey, which can be
dynamically altered based on frequency in customer survey responses
and generated manually or via algorithms from machine learning or
artificial intelligence. These attributes make it easier for
customers to give feedback by simply clicking the relevant
attribute, and also make analyzing a mass of customer data easier
by extracting high-frequency low-dimensional signals. Attributes
can further vary by geographies and demographics, allowing for more
granularity in generating the report.
[0094] FIG. 13 shows a GUI 1300 for an authorized user, such as an
operation manager, interested in customer experiences. The tabs at
top display the current user (top right), as well as tabs for an
organizational chart, customer engagement, customer information,
configuration options, and a summary home tab. These tabs are
interactable: they can be clicked and corresponding displays and
ratings data received from customer responses to a survey or
surveys will appear. Overall, GUI 1300 displays a report generated
from data received from customer survey responses.
[0095] The "Home" tab (currently selected) can display information
on customer ratings, surveys, and employees. "Rating snapshot" can
display an aggregate rating by customers as well as more detailed
information on employee ratings (above, below, outside, inside).
Average customer rating can be the mean, median, or other average
of customer ratings. "Rating trends" can show "Customer" as a bar,
displaying average customer ratings by business quarter. In some
embodiments, other time bins can be utilized. "Customer feedback"
can display customer survey responses in more detail, including
average customer rating, the number of customer surveys,
percentages of reviews above and below average, and top attributes
of employees. A drop-down menu can alter the time window whose
information is displayed. "Previous surveys" can show a sampling of
recently--completed surveys and high-level information, including
the year and quarter, attributes, and overall rating. Individual
surveys are interactable and can be clicked for more information.
"My team" can display the number of employees on the workforce and
the number of pursuant surveys.
[0096] FIG. 14 shows an alternate GUI 1400 to the one illustrated
in FIG. 13, displaying only customer-gleaned information. "Rating
snapshot" can display the average customer rating. "Rating trends"
can display the average customer rating over time, such as by
fiscal quarter. "Customer feedback" can break down the average
customer rating by adding top attributes, performance in comparison
to others, and number of surveys received.
[0097] FIG. 15 shows a GUI 1500 for an operation manager when the
"Customers" tab is selected. Under this highest-level tab there can
be sub-tabs, labeled "Trends," "My team," "Locations," "Customers,"
"Responses," and "Surveys." GUI 1500 shows when the sub-tab
"Customers" is selected.
[0098] At the top of GUI 1500, two drop-down menus can be available
which allow a user to decide what customer information should be
displayed. One can set a time window "Last 6 months" and the other
can set a location "Houston Store 1544." Another button "Request
feedback" can allow a user to request feedback from customers
regarding their customer experiences.
[0099] "Happiest customers" can show customers who leave high
ratings overall. In the category summary, averages can be shown for
the average overall rating as well as the average survey count per
customer. Individual customer data can be shown as well, displaying
the customer name, contact information, average rating, and number
of surveys. "Least satisfied customers" can display the same
information, but for customers who leave low ratings overall.
[0100] "Customer details" can allow users to choose a subset of
customer information to view. Users can choose filter fields from
drop down menus, choose thresholds, and apply those to the
underlying dataset to view all customers falling within the
specified range. These results can be displayed as a table
including customer ID, name, number of surveys answered, average
customer experience, average employee rating, and number of
locations visited. These results can be exported into a readable
file format, such as a comma-separated value (CSV) file or Excel
(XLS or XLSX) file.
[0101] FIG. 16 shows a GUI 1600 for the "Locations" sub-tab under
the "Customers" tab. The layout can parallel the presentation of
"Customers" (shown in FIG. 15), with "Highest rated locations,"
"Lowest rated locations," and "Location details" paralleling
information in "Happiest customers," "Least satisfied customers,"
and "Customer details," respectively. Information can be filtered
by time (at the top) or by other fields (at the bottom). Data can
be exported to a file for later consumption or analysis. Displayed
categorizations of locations or branches can be different on
different GUIs. In some embodiments, GUI 1600 can display
projections of future performance for locations.
[0102] FIG. 17 shows a GUI 1700 for the "My Team" sub-tab under the
"Customers tab. The layout can parallel in part the presentation of
"Customers" and "Locations" (shown in FIGS. 15 and 16,
respectively).
[0103] "Customer favorites" can display information about favorite
employees as rated by customers. Further, customer favorites can
include average ratings as well as an average number of reviews
received. In addition to aggregated statistics, information about
individual employees can be presented. Such information can include
average customer rating, number of reviews, as well as a top
attribute used to describe an individual employee and the frequency
with which it is assigned in reviews. Employee photos can be shown
for ease of recognition. "Struggling with customers" can parallel
the information in customer favorites, but instead can show
employees with low ratings. These displayed employee
categorizations can be different on different GUIs.
[0104] "Customer ratings by position" can break down average
employee ratings by sub-groups, such as job title. "Position" can
list the job title while "Avg. rating" can show the average rating
for employees in that position. Graphics can be displayed which
show the frequency of ratings on a 1 to 5 scale, using colors, bar
graphs, or other data visualization techniques. In some
embodiments, these data can include projections of future customer
ratings.
[0105] "Employee details" can show information about specific
employees. Field filters can be employed using a drop-down menu,
and thresholds can be set to limit the employee information
displayed. Data can be exported to a file for later consumption or
analysis. In the table, displayed information can include employee
name, average rating, number of ratings, the percentage of ratings
higher than the overall customer experience, and top attributes
with their frequency of mentions in customer reviews.
[0106] FIG. 18 shows a GUI 1800 for the "Responses" sub-tab under
the "Customers" tab. "Customer comments word cloud" can show a
word-cloud using words mined from customer comments. The set of
words chosen can be limited by time and location by using two
drop-down menus.
[0107] "Responses history" can contain customer experiences from
the selected locations in the selected timeframes. It can provide a
list of customer experiences with details including customer
experience scores, dates, and times. These data can be exported for
later consumption or analysis. A search bar can allow for specific
customer experiences to be sought out.
[0108] When an individual customer experience is selected, the
display can show more in-depth information. Such information can
include location, customer email, customer phone, customer name,
notes, when the survey was sent, when the response was received,
customer experience rating and attributes, employee name, employee
rating and attributes, and customer comments.
[0109] FIG. 19 shows a GUI 1900 for the "Trends" sub-tab under the
"Customers" tab. Like other aspects of the GUI, data can be
filtered by time and location, and can be exported for later
consumption or analysis.
[0110] "Average CX Rating" can show customer experience rating
trends through time. Ratings (1 through 5) can be color coded and
stacked in a bar graph, where data can aggregated by month or by
other time bins. The blue line and points can track the average
rating over time, showing the trends. Clicking on an individual
average point can reveal more detailed information for that time
bin: average rating, surveys sent, responses, response rate, number
of each rating 1 through 5, and top positive and negative
attributes.
[0111] "Average response rate" can show the average rate of
response for customer surveys over the time period specified,
aggregated by a specified time bin such as week, month, or business
quarter. "Average employee rating" can do the same for employee
ratings. Clicking on an individual average point can reveal more
detailed information for that time bin.
[0112] FIG. 20 shows a GUI 2000 for the "Surveys" sub-tab under the
"Customers" tab. "New customer survey" can allow a user to submit a
survey to a customer for completion. Fields to specify can include
location, customer email, customer phone number, customer name,
employee (singular or plural), and notes. Clicking "Submit" can
send the survey to the specified customer for completion.
[0113] FIG. 21 shows a customer mobile device GUI 2100 with a
notification inviting the customer to complete a customer
experience survey. The notification can include the name of the
business, a message asking for feedback, and a link to the survey.
The notification can be sent via the GUI shown in FIG. 20.
[0114] FIG. 22 shows a customer mobile device GUI 2200 after
following the survey invitation presented in FIG. 21. Customers can
be shown the name of the shop and can rate their experience on a
scale of 1 to 5 by selecting the appropriate button. Descriptive
attributes can be selected in the same manner, and more than one
can be selected. Customers can further be shown the name of the
employee who facilitated their customer experience, and can
describe their experience on a scale of 1 to 5. Attributes can be
added similarly to the attributes of the business as a whole.
[0115] In some embodiments, the customer survey responses can be
combined with employee feedback data and employee engagement data
to generate an ordered list of recommended actions for each
individual employee. As portions of the customer survey responses,
employee feedback data, and employee engagement data are specific
to individual employees, these recommended actions can be
specifically-tailored unique to teach employee. A combination of
manual analysis and automated analysis using artificial
intelligence, machine learning, or other models, can order the list
of recommended actions.
[0116] Certain recommended actions can apply to specific categories
of employees. For example, all employees with certain attributes,
all manages in departments with specific problems mentioned in
customer survey data, or all standout performers may receive
category-specific recommended actions. By using the combination of
employee feedback data, employee engagement data, and customer
survey response data to recommend specific actions for employees,
this embodiment can automate portions of enterprise improvement.
Because it is automated, it can also be tweaked, used for NB
testing, or otherwise manipulated to optimize results.
[0117] FIG. 23 illustrates an example method 2300 for determining
customer sentiment ratings. Although the example method 2300
depicts a particular sequence of operations, the sequence may be
altered without departing from the scope of the present disclosure.
For example, some of the operations depicted may be performed in
parallel or in a different sequence that does not materially affect
the function of the method 2300. In other examples, different
components of an example device or system that implements the
method 2300 may perform functions at substantially the same time or
in a specific sequence.
[0118] According to some examples, the method includes receiving
ratings data, the received ratings data comprising responses to a
survey associated with one or more customers of an enterprise and
presenting a fixed number of attributes at step 2302. The received
ratings data can be uniquely associated with the one or more
customers. The received ratings data can comprise one or more of an
overall experience rating, one or more overall experience
attributes, a brand perception rating, one or more brand perception
attributes, a product experience rating, one or more product
experience attributes, an employee rating, one or more employee
attributes, or notes. The survey can include a numeric rating scale
for quantifying a customer sentiment. The middle number of the
numeric rating scale can be presented as visually larger in a
presentation of the survey. The received ratings data can be
pursuant to an employee and can be added to a record for the
employee.
[0119] In another example of the receiving ratings data at step
2302, the method comprises receiving survey parameters, the survey
parameters identifying the one or more customers. Further, the
method comprises sending, to accounts or devices associated with
the one or more customers, a request to respond to the survey.
[0120] According to some examples, the method includes aggregating
the received ratings data at block 2304.
[0121] According to some examples, the method includes generating a
report based on the aggregated ratings data at block 2306.
[0122] In another example of block 2306, the method comprises
analyzing attributes whose attribute frequency rates are above an
attribute frequency threshold. Further, the method comprises
dynamically adjusting attribute presentation rates in the survey
based in part on the attribute frequency rates for the attributes.
The method can include using attributes whose attribute frequency
rates in open comments are above an open-comment attribute
frequency threshold to generate additions to the attribute list.
The method can include tracking the attribute frequency rates for
the attributes from the attribute list and removing attributes from
the attribute list whose attribute frequency rates are below an
attribute frequency removal threshold. The method can include using
artificial intelligence or manual analysis combined with the
survey, sales data, employee data, or the received ratings data to
guide generation of the attribute list. The method can include
using varied analysis techniques for different geographic regions
or different demographic populations and dynamically varying the
attribute presentation rates based in part on the varied analysis
techniques, the different geographic regions, or the different
demographic populations.
[0123] In another example of block 2306, the method comprises
generating respective scores for one or more employees of the
enterprise, each respective score based at least in part on one or
more responses to the survey. Further, the method comprises
categorizing the one or more employees into performance categories
based on the respective scores. Further, the method comprises
generating a projected performance for the one or more employees
based on the respective scores or the performance categories.
[0124] In another example of block 2306, the method comprises
generating respective scores for one or more branches of the
enterprise, each respective score based at least in part on or more
responses to the survey. Further, the method comprises categorizing
the one or more branches into performance categories based on the
respective scores. Further, the method comprises generating a
projected performance for the one or more branches based on the
respective scores or the performance categories.
[0125] According to some examples, the method includes generating a
navigable interface comprising the generated report, the navigable
interface accessible to an authorized user and comprising tabs,
each tab interactable to display a respective portion of the
generated report at block 108. The at least one interactable
element displayed by at least one of the tabs can allow the
authorized user to generate a new survey. The respective portions
of the generated report displayed by the tabs can contain at least
one interactable element.
[0126] In another example of block 2308, the method comprises
displaying the respective scores or the performance categories
associated with the one or more employees.
[0127] In another example of block 2308, the method comprises
displaying the respective scores or the performance categories
associated with the one or more branches.
[0128] In the present disclosure, the methods disclosed may be
implemented as sets of instructions or software readable by a
device. Further, it is understood that the specific order or
hierarchy of steps in the methods disclosed are instances of
example approaches. Based upon design preferences, it is understood
that the specific order or hierarchy of steps in the methods can be
rearranged while remaining within the disclosed subject matter. The
accompanying method claims present elements of the various steps in
a sample order, and are not necessarily meant to be limited to the
specific order or hierarchy presented.
[0129] The described disclosure may be provided as a computer
program product, or software, that may include a computer-readable
storage medium having stored thereon instructions, which may be
used to program a computer system (or other electronic devices) to
perform a process according to the present disclosure. A
computer-readable storage medium includes any mechanism for storing
information in a form (e.g., software, processing application)
readable by a computer. The computer-readable storage medium may
include, but is not limited to, optical storage medium (e.g.,
CD-ROM), magneto-optical storage medium, read only memory (ROM),
random access memory (RAM), erasable programmable memory (e.g.,
EPROM and EEPROM), flash memory, or other types of medium suitable
for storing electronic instructions.
[0130] Although the present technology has been described in detail
for the purpose of illustration based on what is currently
considered to be the most practical and preferred implementations,
it is to be understood that such detail is solely for that purpose
and that the technology is not limited to the disclosed
implementations, but, on the contrary, is intended to cover
modifications and equivalent arrangements that are within the
spirit and scope of the appended claims. For example, it is to be
understood that the present technology contemplates that, to the
extent possible, one or more features of any implementation can be
combined with one or more features of any other implementation.
* * * * *